Merge remote-tracking branch 'upstream/main'

reasoning_gym/algorithmic/word_ladder.py

@@ -1,13 +1,15 @@
 """Word ladder task generator"""
 
+from collections import deque
 from dataclasses import dataclass
 from random import Random
-from typing import List, Optional, Set, Dict, Tuple
-from collections import deque
+from typing import Dict, List, Optional, Set, Tuple
+
 from reasoning_gym.data import read_data_file
 
 from ..factory import ProceduralDataset, register_dataset
 
+
 @dataclass
 class WordLadderConfig:
     """Configuration for word ladder task generation"""
@@ -32,7 +34,9 @@ class WordLadderConfig:
         # Modified validation logic
         if self.min_chain_length == -1:
             if self.max_chain_length != -1:
-                assert self.max_chain_length >= 3, "When min_chain_length=-1 (shortest path), max_chain_length must be -1 or >=3"
+                assert (
+                    self.max_chain_length >= 3
+                ), "When min_chain_length=-1 (shortest path), max_chain_length must be -1 or >=3"
         elif self.max_chain_length == -1:
             raise AssertionError("max_chain_length cannot be -1 unless min_chain_length is also -1")
         else:
@@ -81,16 +85,17 @@ class WordLadderDataset(ProceduralDataset):
         
         import csv
         from io import StringIO
+
         word_sets = {}
-        
+
         try:
             # Get CSV content as string
             csv_content = read_data_file("words.csv")
-            
+
             # Use StringIO to create a file-like object from the string
             csv_file = StringIO(csv_content)
             reader = csv.DictReader(csv_file)
-            
+
             for row in reader:
                 # Process each word length column using config range
                 for length in range(min_length, max_length + 1):
@@ -104,12 +109,12 @@ class WordLadderDataset(ProceduralDataset):
                         
         except Exception as e:
             raise RuntimeError(f"Error processing words.csv content: {e}") from e
-        
+
         # Validate we have words for each length
         for length in range(min_length, max_length + 1):
             if length not in word_sets or not word_sets[length]:
                 raise ValueError(f"No valid words found for length {length}")
-                
+
         return word_sets
 
     def _get_neighbors(self, word: str, word_set: Set[str]) -> Set[str]:
@@ -160,7 +165,7 @@ class WordLadderDataset(ProceduralDataset):
         # Use basic BFS for shortest path
         queue = deque([(start, [start])])
         visited = {start}
-        
+
         while queue:
             current, path = queue.popleft()
             if current == end:
@@ -212,12 +217,7 @@ class WordLadderDataset(ProceduralDataset):
         return {
             "question": f"Transform the word ladder '{start}' to '{end}' by changing one letter at a time.",
             "answer": ",".join(path),
-            "metadata": {
-                "start_word": start,
-                "end_word": end,
-                "word_length": length,
-                "chain_length": len(path)
-            }
+            "metadata": {"start_word": start, "end_word": end, "word_length": length, "chain_length": len(path)},
         }
 
-register_dataset("word_ladder", WordLadderDataset, WordLadderConfig)
+register_dataset("word_ladder", WordLadderDataset, WordLadderConfig)